概念 ：中央任务调度中枢

• ✅ 优点：全局资源协调，确保任务执行顺序
• ❌ 缺点：单点故障风险，可能成为性能瓶颈

import operator
import os

from langchain.schema import SystemMessage, HumanMessage
from langchain_openai import ChatOpenAI
from langgraph.graph import StateGraph, START, END
from langgraph.constants import Send
from pydantic import BaseModel, Field
from typing_extensions import Annotated, TypedDict, List

# 将一个论证标题拆分为多个子标题，大模型并行执行这些子标题，最终汇总所有的结果。

# 初始化模型
llm = ChatOpenAI(
    model="gpt-3.5-turbo",
    openai_api_key=os.environ["GPT_API_KEY"],
    openai_api_base="https://api.chatanywhere.tech/v1",
    streaming=False  # 禁用流式传输
)

# 大模型拆分的某一个维度的结构
class Section(BaseModel):
    name: str = Field(description="章节标题")
    description: str = Field(description="章节概述")

# 结构化输出信息的实体类，输出一个列表
class Sections(BaseModel):
    sections: List[Section] = Field(description="章节列表")

# 大模型用到的一些参数
class State(TypedDict):
    topic: str
    sections: list[Section]
    completed_sections: Annotated[list, operator.add]
    final_report: str

# 大模型并行执行的任务参数
class WorkerState(TypedDict):
    section: Section
    completed_sections: Annotated[list, operator.add]

# 大模型的调度器，将任务拆分
def orchestrator(state: State):
    print("大模型调度器开始拆分任务")
    new_llm = llm.with_structured_output(Sections, method="function_calling")
    output = new_llm.invoke(
        [
            SystemMessage(content="你需要分析论证某个主题，并将其拆分成几个不同的立场进行多视角分析。"),
            HumanMessage(content=f"论证主题：{state['topic']}")
        ]
    )
    print(f"大模型调度器任务拆分完成： {output.sections}")
    return {"sections": output.sections}

# 大模型执行的具体任务
def llm_call(state: WorkerState):
    print(f"大模型执行任务： {state['section'].name}")
    output = llm.invoke(
        [
            SystemMessage(content="根据提供的章节标题和概述，完成论证文章中的其中一个章节。"),
            HumanMessage(content=f"章节标题为：{state['section'].name} 章节概述为：{state['section'].description}")
        ]
    )
    return {"completed_sections": [output.content]}

# 大模型合成最终结果
def synthesizer(state: WorkerState):
    print(f"大模型合成最终结果")
    completed_sections = state["completed_sections"]
    completed_report_sections = "\n\n---\n\n".join(completed_sections)
    return {"final_report": completed_report_sections}

# 分配到多个任务上
def assign_worker(state: State):
    # 需要多个任务并发，但是并不清楚有多少个任务时，使用Send
    return [Send("llm_call", {"section": s}) for s in state["sections"]]

# 创建工作流
work_flow = StateGraph(State)
work_flow.add_node("orchestrator", orchestrator)
work_flow.add_node("llm_call", llm_call)
work_flow.add_node("synthesizer", synthesizer)

work_flow.add_edge(START, "orchestrator")
work_flow.add_conditional_edges(
    "orchestrator",
    assign_worker,
    ["llm_call"]
)
work_flow.add_edge("llm_call", "synthesizer")
work_flow.add_edge("synthesizer", END)

graph = work_flow.compile()

result = graph.invoke({"topic": "如何评价ChatGPT"})
print(f"最终结果： {result['final_report']}")

执行结果